Estimating Displaced Populations from Overhead

• URL: http://arxiv.org/abs/2006.14547v2
• Date: Mon, 21 Dec 2020 17:41:21 GMT
• Title: Estimating Displaced Populations from Overhead
• Authors: Armin Hadzic, Gordon Christie, Jeffrey Freeman, Amber Dismer, Stevan Bullard, Ashley Greiner, Nathan Jacobs, Ryan Mukherjee
• Abstract summary: We train and evaluate our approach on drone imagery cross-referenced with population data for refugee camps in Cox's Bazar, Bangladesh. Our proposed approach achieves 7.02% mean absolute percent error on sequestered camp imagery.
• Score: 12.392630992454396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a deep learning approach to perform fine-grained population estimation for displacement camps using high-resolution overhead imagery. We train and evaluate our approach on drone imagery cross-referenced with population data for refugee camps in Cox's Bazar, Bangladesh in 2018 and 2019. Our proposed approach achieves 7.02% mean absolute percent error on sequestered camp imagery. We believe our experiments with real-world displacement camp data constitute an important step towards the development of tools that enable the humanitarian community to effectively and rapidly respond to the global displacement crisis.

Related papers

• Mapping Reduced Accessibility to WASH Facilities in Rohingya Refugee Camps With Sub-Meter Imagery [13.938740503013257]
  Access to Water, Sanitation, and Hygiene (WASH) services remains a major public health concern in refugee camps.<n>This study introduces a remote sensing-driven framework to quantify WASH accessibility-specifically to water pumps, latrines, and bathing cubicles.<n>Using sub-meter satellite images, we develop a semi-supervised segmentation framework that achieves an F1-score of 76.4% in detecting individual refugee shelters.
  arXiv  Detail & Related papers  (2025-11-10T15:48:04Z)
• Gridding Forced Displacement using Semi-Supervised Learning [0.0]
  We present a semi-supervised approach that disaggregates refugee statistics from administrative boundaries to 0.5-degree grid cells across 25 Sub-Saharan African countries.<n>By integrating UNHCR's ProGres registration data with satellite-derived building footprints from Google Open Buildings and location coordinates from OpenStreetMap Populated Places, our label spreading algorithm creates spatially explicit refugee statistics at high granularity.
  arXiv  Detail & Related papers  (2025-05-19T09:27:58Z)
• ProgRoCC: A Progressive Approach to Rough Crowd Counting [66.09510514180593]
  We label Rough Crowd Counting that delivers better accuracy on the basis of training data that is easier to acquire. We propose an approach to the rough crowd counting problem based on CLIP, termed ProgRoCC. Specifically, we introduce a progressive estimation learning strategy that determines the object count through a coarse-to-fine approach.
  arXiv  Detail & Related papers  (2025-04-18T01:57:42Z)
• Crowd3D: Towards Hundreds of People Reconstruction from a Single Image [57.58149031283827]
  We propose Crowd3D, the first framework to reconstruct the 3D poses, shapes and locations of hundreds of people with global consistency from a single large-scene image. To deal with a large number of persons and various human sizes, we also design an adaptive human-centric cropping scheme.
  arXiv  Detail & Related papers  (2023-01-23T11:45:27Z)
• Releasing survey microdata with exact cluster locations and additional privacy safeguards [77.34726150561087]
  We propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards. Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80% even under re-identification attempts.
  arXiv  Detail & Related papers  (2022-05-24T19:37:11Z)
• So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale [11.38584315242023]
  We provide a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative.
  arXiv  Detail & Related papers  (2022-04-07T07:30:43Z)
• IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling [90.97859312029615]
  We propose an approach to estimate object count statistics over large geographies through sampling. We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U.S.
  arXiv  Detail & Related papers  (2021-12-16T18:59:29Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• Census-Independent Population Estimation using Representation Learning [0.5735035463793007]
  Census-independent population estimation approaches using alternative data sources have shown promise in providing frequent and reliable population estimates locally. We explore recent representation learning approaches, and assess the transferability of representations to population estimation in Mozambique. Using representation learning reduces required human supervision, since features are extracted automatically. We compare the resulting population estimates to existing population products from GRID3, Facebook (HRSL) and WorldPop.
  arXiv  Detail & Related papers  (2021-10-06T15:13:36Z)
• Assessing the Quality of Gridded Population Data for Quantifying the Population Living in Deprived Communities [0.0]
  In 2014 on average 65% of the urban population lived in slums. Most of the data about slums comes from census data, which is only available at aggregate levels and often excludes these settlements. We evaluate the accuracy of the WorldPOP and LandScan population layers against ground-truth data composed of 1,703 georeferenced polygons.
  arXiv  Detail & Related papers  (2020-11-25T18:14:30Z)
• Population Mapping in Informal Settlements with High-Resolution Satellite Imagery and Equitable Ground-Truth [1.4414055798999759]
  We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas. We use equitable ground-truth data, which is gathered in collaboration with local communities. We propose a gridded population estimation model, enabling flexible and customizable spatial resolutions.
  arXiv  Detail & Related papers  (2020-09-17T16:37:32Z)
• Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient [72.44260113860061]
  We propose an interactive method to support and optimize the work of volunteers in OpenStreetMap. The proposed method greatly reduces the amount of data that the volunteers of OSM need to verify/correct.
  arXiv  Detail & Related papers  (2020-09-17T10:05:30Z)
• Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis [2.1793210447846776]
  Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country. Non-government organizations and local government units are faced with the challenge of identifying, assessing, and monitoring rapidly growing migrant communities. We propose a novel approach for locating new and emerging informal settlements using machine learning and publicly accessible Sentinel-2 time-series satellite imagery.
  arXiv  Detail & Related papers  (2020-08-27T04:42:45Z)
• Give more data, awareness and control to individual citizens, and they will help COVID-19 containment [74.10257867142049]
  Contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach raises concerns about citizens' privacy and needlessly strong digital surveillance. We advocate a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores"
  arXiv  Detail & Related papers  (2020-04-10T20:30:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.